{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance \n",
    "\n",
    "## bi-lstm_lr-0.5_training_stats\n",
    "Accuracy: 0.8800959232613909  \n",
    "Predicted tags: 9591  \n",
    "Number of POS tags: 18  \n",
    "\n",
    "## lstm_lr-0.5_training_stats\n",
    "Accuracy: 0.8331769367115004  \n",
    "Predicted tags: 9591  \n",
    "Number of POS tags: 18  \n",
    "\n",
    "## bi-lstm_lr-0.1_training_stats\n",
    "Accuracy: 0.7562297987696799  \n",
    "Predicted tags: 9591  \n",
    "Number of POS tags: 18  \n",
    "\n",
    "## lstm_lr-0.1_training_stats\n",
    "Accuracy: 0.7089980189761235  \n",
    "Predicted tags: 9591  \n",
    "Number of POS tags: 18  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AewBVfRW4BrhJRFxAMTDONwh+WESeAFb4jntctWFHnqXMBQpucRPIVphhGKen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\nRQoPjxxyCBMmQoRiiimhhFJKySWXYPpxsr9PihQ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YpKdnRdZOIJIHLAO+p6pPHvfeM/jB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\nIyP/H29mms4js59XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4222ebb9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jxXPVl0wgOo/9t7ccOfZXY+mRVLvV5HpzuZzLeZ3XGcc43uZtZjKThKihMi4u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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42206f9da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df1 = pd.read_csv(\"bi-lstm_lr-0.5_training_stats.csv\")\n",
    "df2 = pd.read_csv(\"lstm_lr-0.5_training_stats.csv\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.title('Loss Comparison (lr=0.5)')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('avg loss')\n",
    "plt.grid()\n",
    "plt.plot(df1[\"dev_loss\"], \"lime\", label=\"bi-lstm_dev\")\n",
    "plt.plot(df1[\"train_loss\"], \"lightgreen\", label=\"bi-lstm_train\")\n",
    "plt.plot(df2[\"dev_loss\"], \"red\", label=\"lstm_dev\")\n",
    "plt.plot(df2[\"train_loss\"], \"salmon\", label=\"lstm_train\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"figures/lstm_lr-0.5_loss_comparison.png\", dpi=200)\n",
    "plt.show()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.title('Accuracy Comparison (lr=0.5)')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.grid()\n",
    "plt.plot(df1[\"dev_accuracy\"], \"lime\", label=\"bi-lstm_dev\")\n",
    "plt.plot(df1[\"train_accuracy\"], \"lightgreen\", label=\"bi-lstm_train\")\n",
    "plt.plot(df2[\"dev_accuracy\"], \"red\", label=\"lstm_dev\")\n",
    "plt.plot(df2[\"train_accuracy\"], \"salmon\", label=\"lstm_train\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"figures/lstm_lr-0.5_accu_comparison.png\", dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i4Nwe3qsGKv3vzwCW7y69o48+WgfqiSeeUFXVpCb15MTJelPzTRrPxVVVNXPF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\n9h6XlaDwAjA3PqFQJNyYerTBdaq7OI5+D7DBzG6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42255ad3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K12CvFK4ByoDPAScCFwOfzFeiCmGfv4+ACvj5mbPaGGMOBgNeKWRu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\n0h2wILMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4220a2e908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df1 = pd.read_csv(\"bi-lstm_lr-0.1_training_stats.csv\")\n",
    "df2 = pd.read_csv(\"lstm_lr-0.1_training_stats.csv\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.title('Loss Comparison (lr=0.1)')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('avg loss')\n",
    "plt.grid()\n",
    "plt.plot(df1[\"dev_loss\"], \"lime\", label=\"bi-lstm_dev\")\n",
    "plt.plot(df1[\"train_loss\"], \"lightgreen\", label=\"bi-lstm_train\")\n",
    "plt.plot(df2[\"dev_loss\"], \"red\", label=\"lstm_dev\")\n",
    "plt.plot(df2[\"train_loss\"], \"salmon\", label=\"lstm_train\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"figures/lstm_lr-0.1_loss_comparison.png\", dpi=200)\n",
    "plt.show()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.title('Accuracy Comparison (lr=0.1)')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.grid()\n",
    "plt.plot(df1[\"dev_accuracy\"], \"lime\", label=\"bi-lstm_dev\")\n",
    "plt.plot(df1[\"train_accuracy\"], \"lightgreen\", label=\"bi-lstm_train\")\n",
    "plt.plot(df2[\"dev_accuracy\"], \"red\", label=\"lstm_dev\")\n",
    "plt.plot(df2[\"train_accuracy\"], \"salmon\", label=\"lstm_train\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"figures/lstm_lr-0.1_accu_comparison.png\", dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Unnamed: 0  dev_accuracy    dev_loss  train_accuracy  train_loss\n",
      "count  100.000000    100.000000  100.000000      100.000000  100.000000\n",
      "mean    49.500000      0.835094    2.143245        0.869399    2.109559\n",
      "std     29.011492      0.066603    0.064609        0.082517    0.080666\n",
      "min      0.000000      0.579118    2.096154        0.580779    2.045937\n",
      "25%     24.750000      0.767253    2.099317        0.786144    2.047243\n",
      "50%     49.500000      0.879254    2.100676        0.919405    2.059946\n",
      "75%     74.250000      0.880747    2.208516        0.933672    2.190420\n",
      "max     99.000000      0.883653    2.397859        0.935219    2.398944\n",
      "       Unnamed: 0  dev_accuracy    dev_loss  train_accuracy  train_loss\n",
      "count  100.000000    100.000000  100.000000      100.000000  100.000000\n",
      "mean    49.500000      0.807621    2.170420        0.837415    2.139204\n",
      "std     29.011492      0.049190    0.047056        0.064273    0.062674\n",
      "min      0.000000      0.581422    2.140844        0.583975    2.087691\n",
      "25%     24.750000      0.788800    2.144994        0.800435    2.098033\n",
      "50%     49.500000      0.829616    2.148568        0.864319    2.111712\n",
      "75%     74.250000      0.834957    2.186604        0.880483    2.173567\n",
      "max     99.000000      0.840088    2.388319        0.892018    2.387914\n",
      "       Unnamed: 0  dev_accuracy    dev_loss  train_accuracy  train_loss\n",
      "count  100.000000    100.000000  100.000000      100.000000  100.000000\n",
      "mean    49.500000      0.708956    2.266412        0.720729    2.255798\n",
      "std     29.011492      0.070116    0.071132        0.076954    0.077956\n",
      "min      0.000000      0.369882    2.212988        0.371933    2.191851\n",
      "25%     24.750000      0.662059    2.214226        0.663953    2.196361\n",
      "50%     49.500000      0.733742    2.241738        0.747349    2.228804\n",
      "75%     74.250000      0.761677    2.309087        0.781729    2.306882\n",
      "max     99.000000      0.763431    2.640541        0.785564    2.642333\n",
      "       Unnamed: 0  dev_accuracy    dev_loss  train_accuracy  train_loss\n",
      "count  100.000000    100.000000  100.000000      100.000000  100.000000\n",
      "mean    49.500000      0.696160    2.278271        0.708127    2.267574\n",
      "std     29.011492      0.062834    0.063487        0.067110    0.068161\n",
      "min      0.000000      0.296366    2.257034        0.295323    2.237067\n",
      "25%     24.750000      0.708739    2.258536        0.712278    2.241042\n",
      "50%     49.500000      0.714106    2.259680        0.727849    2.247539\n",
      "75%     74.250000      0.715494    2.265065        0.734390    2.262646\n",
      "max     99.000000      0.716934    2.684550        0.738582    2.687977\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"bi-lstm_lr-0.5_training_stats.csv\")\n",
    "df2 = pd.read_csv(\"lstm_lr-0.5_training_stats.csv\")\n",
    "df3 = pd.read_csv(\"bi-lstm_lr-0.1_training_stats.csv\")\n",
    "df4 = pd.read_csv(\"lstm_lr-0.1_training_stats.csv\")\n",
    "\n",
    "print(df1.describe())\n",
    "print(df2.describe())\n",
    "print(df3.describe())\n",
    "print(df4.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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